论文标题
FASTVA：通过Edge Processing和移动中的NPU进行深度学习视频分析
FastVA: Deep Learning Video Analytics Through Edge Processing and NPU in Mobile
论文作者
论文摘要
已经开发了许多移动应用程序将深度学习应用于视频分析。尽管这些先进的深度学习模型可以为我们提供更好的结果，但它们也遭受了高计算开销的困扰，这意味着在移动设备上运行时更长的延迟和更多的能量消耗。要解决此问题，我们提出了一个名为FastVA的框架，该框架通过移动中的Edge Grocessing和Neural Grocessing和Neural Grocessing单元（NPU）支持深度学习视频分析。主要的挑战是确定何时卸载计算以及何时使用NPU。基于移动应用程序的处理时间和准确性要求，我们研究了两个问题：最大准确性是在某些时间限制下的目标是最大化准确性，而最大限制的目标是最大化效用，这是处理时间和精度的加权功能。我们将它们作为整数编程问题提出，并提出基于启发式方法的解决方案。我们已经在智能手机上实施了FASTVA，并通过广泛的评估证明了其有效性。
Many mobile applications have been developed to apply deep learning for video analytics. Although these advanced deep learning models can provide us with better results, they also suffer from the high computational overhead which means longer delay and more energy consumption when running on mobile devices.To address this issue, we propose a framework called FastVA, which supports deep learning video analytics through edge processing and Neural Processing Unit (NPU) in mobile. The major challenge is to determine when to offload the computation and when to use NPU. Based on the processing time and accuracy requirement of the mobile application, we study two problems: Max-Accuracy where the goal is to maximize the accuracy under some time constraints, and Max-Utility where the goal is to maximize the utility which is a weighted function of processing time and accuracy. We formulate them as integer programming problems and propose heuristics based solutions. We have implemented FastVA on smartphones and demonstrated its effectiveness through extensive evaluations.
